US7599866B2 - Simultaneous optimal auctions using augmented lagrangian and surrogate optimization - Google Patents
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Definitions
- a method for determining a market price, subject to a plurality of constraints and an actual purchase cost comprising the steps of defining an objective function relating the market price and constraints in terms of a nonlinear programming expression, and minimizing the objective function consistent with the actual purchase cost.
- the objective function comprises a sum over all market suppliers of the market price times the amount purchased plus the supplier's capacity related cost.
- the method can be extended to include transmission network constraints.
- the market price can be a market clearing price, a locational marginal price, or a treasury bill price.
- the step of minimizing the objective function comprises the steps of applying Lagrangian relaxation to the nonlinear programming expression to form a Lagrangian dual function; resolving the Lagrangian dual function using a surrogate optimization framework comprising repeating, until stopping criteria are satisfied, the steps of forming one or more supply sub-problems and a market price sub-problem; optimizing the market price sub-problem while keeping all other variables at their latest available values; optimizing at least one of the one or more supply sub-problems while keeping all other variables at their latest available values; and updating multipliers used to relax constraints; and determining the market price in accordance with the resolved Lagrangian dual function.
- the method comprises the step of adding penalty terms to the Lagrangian dual function to form an augmented Lagrangian dual function.
- One or more sub-problems may be solved by using backward dynamic programming.
- the Lagrangian dual function may be resolved using a surrogate optimization framework comprising repeating, until stopping criteria are satisfied, the steps of forming one or more supply sub-problems; determining a maximum selected supply offer by optimizing at least one of the one or more supply sub-problems while keeping all other variables at their latest available values; setting the market price equal to the maximum selected supply offer while keeping all other variables at their latest available values; updating multipliers used to relax constraints; and determining the market price in accordance with the resolved Lagrangian dual function.
- a surrogate optimization framework comprising repeating, until stopping criteria are satisfied, the steps of forming one or more supply sub-problems; determining a maximum selected supply offer by optimizing at least one of the one or more supply sub-problems while keeping all other variables at their latest available values; setting the market price equal to the maximum selected supply offer while keeping all other variables at their latest available values; updating multipliers used to relax constraints; and determining the market price in accordance with the resolved Lagrangian dual function.
- a method for determining a market clearing price (MCP) in an electricity market is described, subject to a plurality of constraints, comprising the steps of defining an objective function relating the MCP and constraints in terms of a nonlinear programming expression, applying Lagrangian relaxation to the nonlinear programming expression to form a Lagrangian dual function; adding penalty terms to the Lagrangian dual function to form an augmented Lagrangian dual function; resolving the augmented Lagrangian dual function using a surrogate optimization framework comprising repeating, until stopping criteria are satisfied, the steps of forming one or more supply sub-problems and an MCP sub-problem; optimizing the MCP sub-problem while keeping all other variables at their latest available values; optimizing at least one of the one or more supply sub-problems while keeping all other variables at their latest available values; and updating multipliers used to relax constraints; determining the MCP in accordance with the resolved augmented Lagrangian dual function; and paying for electricity at the MCP.
- MCP market clearing price
- the method of the invention can be applied to demand bids, capacity compensation, and ancillary services.
- the objective function then comprises a sum over all market suppliers of the market price for ancillary services times the amount purchased plus a capacity compensation for the supplier's capacity related cost.
- the ancillary services in various embodiments may comprise one or more of energy, regulation, spinning reserve, or non-spinning reserve.
- the step of minimizing the objective function may then comprise the steps of applying Lagrangian relaxation to the nonlinear programming expression to form a Lagrangian dual function; resolving the Lagrangian dual function using a surrogate optimization framework comprising repeating, until stopping criteria are satisfied, the steps of forming one or more demand sub-problems and a market price sub-problem; optimizing the market price sub-problem while keeping all other variables at their latest available values; optimizing at least one of the one or more demand sub-problems while keeping all other variables at their latest available values; and updating multipliers used to relax constraints; and determining the market price in accordance with the resolved Lagrangian dual function.
- a surrogate optimization framework comprising repeating, until stopping criteria are satisfied, the steps of forming one or more demand sub-problems and a market price sub-problem; optimizing the market price sub-problem while keeping all other variables at their latest available values; optimizing at least one of the one or more demand sub-problems while keeping all other variables at their latest available values; and updating multipliers used to relax constraints
- a system for determining a market price, subject to a plurality of constraints and an actual purchase cost, comprising a data processing system in communication with a mass storage device; a computer readable mass storage medium useable in the mass storage device, containing program instructions: encoding an objective function relating the market price and constraints in terms of a nonlinear programming expression, and sufficient to cause the data processing system to perform the step of minimizing the objective function consistent with the actual purchase cost.
- a system for determining a market clearing price (MCP) in an electricity market comprising a data processing system in communication with a mass storage device; a computer readable mass storage medium useable in the mass storage device, containing program instructions: encoding an objective function relating the MCP and constraints in terms of a nonlinear programming expression, and sufficient to cause the data processing system to perform the steps of resolving the nonlinear programming expression using a surrogate optimization framework; and determining the MCP in accordance with the resolved nonlinear programming expression.
- a computer readable mass storage medium useable in a mass storage device to determine a market price is described, subject to a plurality of constraints and an actual purchase cost, containing program instructions encoding an objective function relating the market price and constraints in terms of a nonlinear programming expression, and sufficient to cause a data processing system to perform the step of minimizing the objective function consistent with the actual purchase cost.
- FIG. 1 is a graph of hourly MCPs for the Pay-as-Offer and the Pay-at-MCP formulations for Example 3.
- FIG. 2 is a graph of decision regions used in solving MCP sub-problems.
- FIG. 3 is a flow chart of one embodiment of the method of the present invention.
- FIG. 4 is a flow diagram of an embodiment of the method of the invention incorporating transmission constraints.
- FIG. 5 illustrates a typical data processing system upon which one embodiment of the present invention is implemented.
- FIG. 6 is Equation A.12.
- a traditional unit commitment of a power system with thermal, hydro, and pumped storage generation units, and bilateral contracts is used to determine when to start up and/or shut down generation units, or take contracted energy, and how to dispatch the committed units and contracts to meet system demand and reserve requirements over a particular time period.
- Each unit or contract may have limited energy, minimum up/down times, and/or other constraints.
- the objective is to minimize the total generation cost.
- the traditional unit commitment itself is an NP hard problem, i.e., the computational requirements of obtaining an optimal solution grow exponentially with the size of the problem (e.g., the number of generators involved).
- the separable structure of the problem is a key factor for Lagrangian relaxation to be effective.
- a problem is separable and can be decomposed into multiple sub-problems if both the objective function and the constraints that couple sub-problems are additive in terms of sub-problem decision variables.
- the disadvantage of this method is that the dual solution is generally infeasible, i.e., the once relaxed system constraints are not satisfied. Heuristics are needed to modify subproblem solutions to obtain a good feasible schedule. Nevertheless, since the value of the dual function is a lower bound on the optimal cost, the quality of the feasible solution can be quantitatively evaluated.
- the limitations of the transmission network can also be defined as system constraints and incorporated into the Lagrangian relaxation framework.
- the Lagrangian relaxation technique has been well developed and applied to the unit commitment problem with a complex set of system constraints and individual generation/transaction constraints.
- the method has been shown to work, for example, in cases based on expected operational data for an ISO in the northeast United States. Many commercial software programs are available to solve variations of this problem.
- the objective of the ISO is to select supply offers and their associated power levels over the specified time period T so that the system demand and the minimum/maximum power level constraints are satisfied at the minimum cost.
- the market clearing price (MCP) for a particular time is then obtained as the maximum offer price of all selected participants.
- MCP market clearing price
- the fuel cost of a thermal unit is usually modeled as a quadratic function or a piecewise linear function of the generation level, and the start-up cost as an exponential or linear function of time since last shut down.
- the fuel cost or transaction cost C i (p i (t)) and start-up cost or capacity-related cost S i (t) are assumed in this disclosure to be piecewise linear and linear functions, respectively.
- u i (t) is defined to represent the status of a supply offer.
- the offer is considered “on” (1) if it is selected and “off” (0) if it is not selected.
- Electricity is frequently described as different from other commodities.
- Electricity markets are not only used to sell or buy energy (electrons) but also ancillary services necessary to maintain system reliability (e.g., regulation up, regulation down, spinning reserve, non-spinning reserve, and replacement reserve, in California).
- energy usage is competing against ancillary services for the same generation capacity, while the provision of committed energy and ancillary services must simultaneously be feasible and satisfy all of the transmission network constraints.
- the California market was designed to auction energy and deal with transmission line congestion using a set of adjustment bids different from the energy bids, and subsequently auction each of ancillary services separately in day-ahead and hour ahead markets, based on a market-clearing price mechanism. This sequential and segregated nature significantly deteriorated the efficiency of the California electricity market, and contributed to huge costs for California consumers during the 2000-01 energy crisis.
- the other deregulated energy markets such as the PJM ISO and NY-ISO, have been designed differently using a simultaneous, market-clearing optimal auction.
- simultaneous optimal auction for example, generators bid their generation into the day-ahead market in terms of energy price curves, startup cost curves, minimum and maximum generation levels, and ramping rates.
- Hourly energy and ancillary services are procured and paid at market-clearing prices for these services to meet the demand and reserve requirement while satisfying transmission constraints.
- the Pay-as-offer objective function of Eq. (1) intrinsically implies that ISOs will pay selected participants their offer prices (the Pay-as-offer mechanism). However, most ISOs settle their markets using the Pay-at-MCP mechanism, where the MCPs are used to pay all participants who provide the energy.
- the purchase cost could be significantly higher than the cost obtained from the minimization in Eq. (1), since participants with supply offers lower than the MCP are paid at the MCP.
- a function f(t) can be defined as:
- this is a simultaneous optimal auction based on a market clearing price mechanism formulated so as to minimize the total procurement cost for energy and ancillary services.
- This optimization is subject to the constraints shown in Eq. (2), Eq. (3) and Eq. (4), where the MCP is defined as in Eq. (5).
- the Pay-at-MCP objective function is complicated because it is a function of both the MCPs and power levels of selected supply offers, while MCPs themselves are yet to be determined (endogenous) based on selected offer curves per Eq. (5). Furthermore, the existence of cross product terms of MCPs and selected power levels in Eq. (6) makes the problem inseparable. (Here, ⁇ p i ⁇ and ⁇ MCP(t) ⁇ are treated as decision variables. There are other ways to look at the problem by exploiting special features of the formulation. However, the method presented here is generic and robust.) Consequently, direct application of Lagrangian relaxation may not be effective.
- MCP—Offer constraints MCP “minus” Offer
- MCP “minus” Offer in linear inequality form: MCP ( t ) ⁇ O i r ( p i ( t ), t ), ⁇ i and t, (9a) or equivalently, g i ( t ) ⁇ O i r ( p i ( t ), t ) ⁇ MCP ( t ) ⁇ 0 , ⁇ i and t.
- MCP—Offer constraints MCP ( t ) ⁇ O i r ( p i ( t ), t ) ⁇ MCP ( t ) ⁇ 0 , ⁇ i and t.
- the problem formulation of a simultaneous optimal auction with a market clearing price mechanism (the “Pay-at-MCP problem”) is different from the unit commitment problem, and difficult to solve.
- an augmented Lagrangian relaxation method is developed to solve the Pay-at-MCP problem within a surrogate optimization framework.
- the augmented Lagrangian as opposed to the standard Lagrangian can be used in view that linear sub-problem objective functions will cause solution oscillations when the standard Lagrangian is used. While it is possible to proceed with the standard Lagrangian, these difficulties are overcome by using the augmented Lagrangian, which is formed by adding penalty terms associated with equality and inequality constraints to the Lagrangian. Preferably these penalty terms are quadratic, but other penalty terms, including exponential, are known in the art with reference to this disclosure, and could be useable in the present invention.
- a surrogate optimization framework may be used to overcome the difficulty of inseparability.
- the key idea is to pull out all the terms associated with a supply offer from the augmented Lagrangian to form a supply sub-problem, and decision variables for this offer are optimized while keeping all other variables such as MCPs at their latest available values.
- the MCP sub-problem for time t is similarly formed by pulling out all the terms containing MCP(t). It is then solved by optimizing MCP(t) while keeping all other variables such as power levels ⁇ p i (t) ⁇ at their latest available values.
- the multipliers used to relax constraints are updated after solving just one or a few sub-problems under this surrogate framework.
- the above augmented Lagrangian is to be minimized by selecting appropriate ⁇ MCP(t) ⁇ , ⁇ p i (t) ⁇ , and ⁇ z i 2 (t) ⁇ .
- the process is to first select z i 2 (t) subject to z i 2 (t) ⁇ 0, and the resulting augmented Lagrangian can be simplified to:
- the sub-problem for supply offer i is formed from Eq. (D.2) by pulling out all the terms related to offer i, i.e., ⁇ p i (t) ⁇ and ⁇ S i (t) ⁇ .
- Eq. (D.2) By expanding the terms in Eq. (D.2), the offer i sub-problem is obtained as:
- BDP backward dynamic programming
- T MCP sub-problems are formed, one for each t, by expanding Eq. (D.2) and collecting all the terms involving MCP(t), i.e.,
- L MCP(t) in Eq. (D.4) is the sum of a linear term ( ⁇ p i (t)) ⁇ MCP(t) and quadratic terms which depend on the magnitudes of ⁇ a i (t) ⁇ for individual offers.
- Eq. (D.4) includes a quadratic term of MCP(t) in the form of a i (t) 2 /2c.
- k i (t) defined as k i ( t ) ⁇ [ ⁇ i ( t )/ c+O i r ( p i ( t ), t )]
- Eq. (D.4) takes zero value.
- the second term on the right-hand-side of Eq. (D.4) represents many “half quadratics” delineated by ⁇ k i (t) ⁇ as shown in FIG. 2 , and the MCP(t) axis is divided into multiple decision regions, each with a quadratic L MCP(t) .
- the sub-problem for MCP(t) is first solved for each region by minimizing the corresponding quadratic function, and the costs of individual regions are then compared to obtain the optimal MCP(t).
- the surrogate subgradient component for time t with respect to the system demand multiplier ⁇ (t) is obtained from Eq. (D.2) as:
- the level of constraint violation for hour t with respect to the system demand equality constraint is given by the absolute value of S ⁇ (t), i.e.,
- S ⁇ i (t) is negative, the inequality constraint is satisfied and there is no constraint violation.
- S ⁇ i (t) is positive then the inequality constraint is violated. Consequently, the level of constraint violation is given by max(0,S ⁇ i (t)).
- the level of constraint violation for the entire problem can thus be measured by the L-2 norm of the following constraint violation vector:
- the Lagrangian dual function may be resolved using a surrogate optimization framework comprising repeating, until stopping criteria are satisfied, the steps of forming one or more supply sub-problems; determining a maximum selected supply offer by optimizing at least one of the one or more supply sub-problems while keeping all other variables at their latest available values; setting the market price equal to the maximum selected supply offer while keeping all other variables at their latest available values; updating multipliers used to relax constraints; and determining the market price in accordance with the resolved Lagrangian dual function.
- a surrogate optimization framework comprising repeating, until stopping criteria are satisfied, the steps of forming one or more supply sub-problems; determining a maximum selected supply offer by optimizing at least one of the one or more supply sub-problems while keeping all other variables at their latest available values; setting the market price equal to the maximum selected supply offer while keeping all other variables at their latest available values; updating multipliers used to relax constraints; and determining the market price in accordance with the resolved Lagrangian dual function.
- MCP-Offer constraints for this example are: MCP ⁇ 10; (12) MCP ⁇ 20. (13)
- Augmented Lagrangian Relaxation An augmented Lagrangian is now formed by adding penalty terms (in this example quadratic) of system demand equality constraints Eq. (11) and MCP-Offer inequality constraints Eq. (12) and Eq. (13) to the Lagrangian.
- penalty terms in this example quadratic
- MCP-Offer inequality constraints Eq. (12) and Eq. (13)
- the sub-problem for a supply offer is formed from the augmented Lagrangian by pulling out all the terms related to that offer, i.e., ⁇ p i (t) ⁇ and ⁇ S i (t) ⁇ .
- the decision variables are ⁇ p i (t) ⁇ and decision variables for other sub-problems are taken at their latest available values.
- the offer sub-problem is solved by using backward dynamic programming (BDP), where times (hours) are stages, and the select status (selected or not selected) are states.
- BDP backward dynamic programming
- the startup cost S i (t) is a transition cost which is incurred only when offer i goes from an off state to an on state. All the other costs are stage-wise costs.
- Eq. (16) In solving Eq. (16), p 1 is optimized while MCP and p 2 are taken at their latest available values. Similarly Eq. (17) is solved by optimizing p 2 while MCP and p 1 are taken at their latest available values. Note that without the augmented Lagrangian, c is zero in Eq. (16) and Eq. (17), and the offer sub-problem objective functions are linear. Now with the augmented Lagrangian, c is a positive number, and the objective functions are quadratic which eliminates the solution oscillation difficulties associated with linear objective functions.
- T MCP sub-problems are formed for the different hours by collecting all the terms involving MCP(t).
- MCP(t) is optimized while all other decision variables are taken at their latest available values. Since MCPs appear in all the offer sub-problems, it is recommended that the MCP sub-problems be solved before solving individual offer sub-problems.
- the MCP sub-problem is formed from Eq. (15) by collecting all the terms containing MCP, i.e.,
- the MCP sub-problem is solved by optimizing MCP in Eq. (18) while p 1 and p 2 are considered as given.
- the optimization is not exact but approximate. Consequently, the subgradients obtained are approximate and are referred to as “surrogate subgradients.”
- the multipliers are updated at the high level after one or multiple sub-problems are solved based on the update formula from either the Multiplier Method or the Subgradient Method, with subgradients in the formula replaced by surrogate subgradients.
- the iterative process is terminated if the number of iterations is greater than a preset value or if the level of constraint violation is less than a specified small positive number.
- the Pay-at-MCP/ALSO and the Pay-as-offer algorithms were implemented in C++ on a Pentium-III 500 MHz personal computer with supply offers having single constant segments. For consistency of comparison, an augmented Lagrangian relaxation method was used for the Pay-as-offer algorithm.
- the Numerical results obtained when the Pay-at-MCP/ALSO method was applied to Example 1 are provided in Table 2.
- Example 2 is used to illustrate the subtle differences between the Pay-as-offer and the Pay-at-MCP formulations as well as to provide insights on the convergence and other characteristics of the ALSO algorithm.
- Example 3 demonstrates that the ALSO algorithm is applicable to a medium sized auction problem and significant savings can be achieved over the Pay-as-offer method.
- the least cost for supplying the system demand over the two hours, $6,050, would be obtained if a Pay-as-offer formulation were used and selected participants were paid at their offer prices via a Pay-as-offer mechanism (assuming of course, that their behavior would not change under a Pay-as-offer system, an assumption which may not be realistic). However, paying selected participants using MCPs results in an actual total cost of $16,300, which is significantly higher than the minimized bid cost of $6,050. If the Pay-at-MCP formulation of the invention is used, the minimized costs and the actual costs are equal ($9,300) and lower than the purchase cost obtained from a Pay-as-Offer formulation (a savings of $7,000).
- the Pay-at-MCP/ALSO method is shown to be applicable to a medium sized problem with significant savings being achieved when compared to the Pay-as-offer formulation but with settlements made using the MCP.
- the total supply capacity is 4,620 MW.
- the system demand which is different for each hour over the planning horizon, is given in Table 5 and ranges from low to high values.
- Four nuclear plants with low offer prices (between $30/MW and $37/MW) but with very high startup costs contribute 1,305 MW of the supply capacity.
- Eleven of the offers are from cycling plants, which make up 1,590 MW of the total supply capacity and have prices between $55/MW and $70/MW.
- FIG. 1 shows the plot of the hourly MCPs over the time horizon obtained under the Pay-as-offer and the ALSO algorithms while Table 8 gives a summary of the purchase costs obtained under the different algorithms.
- Table 7 shows that for this example of $218,773 representing a 4.10% savings over the purchase costs associated with the Pay-as-offer formulation.
- Energy costs per year in the U.S. run in the tens of billions of dollars annually in existing ISO markets, therefore, even a 0.1% savings per year represents significant savings of potentially tens of millions of dollars annually.
- the MCPs under the Pay-at-MCP formulation are usually less than the MCPs under the Pay-as-offer formulation. This observation is generally true and translates into lower actual purchase costs under the Pay-at-MCP formulation compared to the costs under the Pay-as-offer formulation as can be seen from Tables 4, 5, and 8.
- an ISO accepts both supply offers and demand bids, and runs auctions for energy and ancillary services to determine the MCPs for each product.
- the total procurement cost for consumers includes the amount paid for energy and ancillary services, together with compensation of start-up and no load costs.
- Further embodiments are presented next for diversified ISO operation environments, including the new formulations and corresponding methodologies to consider demand bids, the simultaneous optimal auctions of energy and ancillary services, and different ways for compensating start-up and no-load costs. Additionally, new formulations and corresponding methodologies are presented to consider transmission network constraints using AC power flow analysis and DC power flow analysis.
- This embodiment presents a new formulation and the corresponding solution methodology for an ISO that accepts both supply offers and demand bids (including energy amount and the associated prices), performs simultaneous auctions for both energy and ancillary services, and does not provide full compensation for capacity related costs (e.g., start-up and no-load costs).
- the formulation includes energy balance constraints, compensation cost function, ancillary service constraints, MCP-offer/purchase constraints, and the objective function.
- the previous MCP formulation Eq. (6) is good if an ISO makes full compensation for start-up and no-load costs.
- compensation is determined by comparing an offer's requested amounts (including applicable start-up and no-load costs) with the revenue generated through MCPs for the day. If the requested amount is less than the revenue generated through MCPs, then no compensation will be made. Only when the requested amount exceeds the revenue generated through MCPs, the difference serves as the compensation term.
- G i ⁇ ⁇ t 1 T ⁇ ⁇ [ C i E ⁇ ( P i E ⁇ ( t ) , t ) + C i R ⁇ ( P i R ⁇ ( t ) , t ) + C i S ⁇ ( P i S ⁇ ( t ) , t ) + C i N ⁇ ( P i N ⁇ ( t ) , t ) + S i NL ⁇ ( t ) + S i ⁇ ( t ) ] , ( A ⁇ .3 ) where P i E (t), P i R (t), P i S (t), and P i N (t) are, respectively, selected energy, regulation, spinning reserve, and non-spinning reserve prices for offer i at hour t; C i E (P i E (t),t), C i R (P i R (t),t), C i S (P i S (t),t) and
- ⁇ i 1 l ⁇ P i N ⁇ ( t ) ⁇ P N ⁇ ( t ) , for ⁇ ⁇ t ⁇ ⁇ from ⁇ ⁇ 1 ⁇ ⁇ to ⁇ ⁇ T , ( A ⁇ .5 )
- P N (t) is system non-spinning reserve requirement at hour t
- P i N (t) is the selected regulation for offer i at time t.
- O i E (P i E (t),t), O i R (P i R (t),t), O i S (P i S (t),t), O i N (P i N (t),t) are, respectively, the offer prices for energy, regulation, spinning reserve, and non-spinning reserve of generation unit i at hour t.
- B m D (t) is the selected energy for bid m at hour t
- B m (P m D (t),t) the corresponding bidding price
- the ISO's objective is to minimize the total purchasing costs.
- the total cost consists of two components: costs paid to offers through MCPs and the compensation of capacity related costs. Therefore the objective function to be minimized is:
- this formulation has more decision variables, i.e., ⁇ MCP E (t) ⁇ , ⁇ MCP R (t) ⁇ , ⁇ MCP S (t) ⁇ , ⁇ MCP N (t) ⁇ , ⁇ P i E (t) ⁇ , ⁇ P i R (t) ⁇ , ⁇ P i S (t) ⁇ , ⁇ P i N (t) ⁇ .
- the compensation term ⁇ tilde over (S) ⁇ i also involves MCPs of energy and ancillary services, and decisions of all selected services of offer i.
- this new formulation is similar to the previous embodiment of the MCP problem. Therefore, this new problem can be similarly solved by using an augmented Lagrangian relaxation and surrogate optimization framework.
- Coupling constraints (Eqs. (A.1) (A.3) (A.4) (A.5) (A.6) (A.7) (A.8) (A.9) (A.10)) are first relaxed, and the augmented Lagrangian is formed in Eq. (A.12) of FIG. 6 .
- Subproblems are then established under the surrogate optimization framework.
- new purchasing subproblems and ancillary service MCP subproblems exist. These subproblems are formed and solved as follows.
- sub-problem for offer i is formed by pulling out all the terms related to offer i's decisions, i.e., ⁇ P i E (t) ⁇ , ⁇ P i R (t) ⁇ , ⁇ P i S (t) ⁇ , and ⁇ P i N (t) ⁇ , from (A.12):
- Eq. (A.2) makes the compensation term ⁇ tilde over (S) ⁇ i couple across the time horizon, consequently, Eq. (A.13) can no longer be decomposed into individual time units (hours) for dynamic programming to be effective.
- One way to overcome this difficulty is to replace ⁇ tilde over (S) ⁇ i by an equivalent expression in terms of two inequality constraints ⁇ tilde over (S) ⁇ i ⁇ 0, and (A.14) ⁇ tilde over (S) ⁇ i ⁇ D i , (A.16)
- BDP Backward dynamic programming
- the purchase sub-problem for demand bid m is formed as a mirror image of an offer sub-problem by pulling out all terms related to decisions of bid m, i.e., ⁇ P m D (t) ⁇ , as
- MCPs for energy and ancillary services need to be determined simultaneously. Therefore, MCP E , MCP R , MCP S , and MCP N sub-problems are formed.
- MCP E (t) subproblem takes the form as:
- MCP R (t), MCP S (t), and MCP N (t) subproblem are formed as follows:
- multipliers will be updated using either the Multiplier Method or the Subgradient Method, with subgradients in the formulas replaced by surrogate subgradients.
- the formulas for the Multiplier Method are:
- Penalty coefficient c is updated by the following formula with ⁇ being a positive constant:
- the next two embodiments incorporate the effects of transmission networks to capture the requirements of Standard Market Design and to ensure stable and secure operations.
- the MCPs may depend on the location, and are therefore referred to as Locational Marginal Prices (LMPs).
- LMPs Locational Marginal Prices
- AC or DC power flow equations are solved. If the result shows that the limit of a transmission line would be exceeded, then sensitivity coefficients are used to form a branch flow inequality constraint for that line.
- Such inequality constraints are then passed as additional constraints to be relaxed in ALSO to avoid violation for future iterations. For simplicity of presentation, system demand is assumed given with no import or export power, ancillary services are not considered, and capacity related costs are fully compensated.
- AC power flow models the nonlinear relationships between bus power injections and bus voltages and phase angles, and has been extensively used in system planning to forecast the effects of contingencies.
- a transmission line connecting two buses i and j in a transmission network Assume that the transmission line is long and can be represented by a ⁇ model.
- the parameters of the transmission line and the buses are given by:
- ⁇ i and ⁇ j Phase angles of bus i and bus j, respectively.
- I Cold vector of injected bus currents of the transmission network.
- V Column vector of injected bus voltages of the transmission network.
- the injected current at node i is given by:
- each bus has four variables associated with it, i.e.,
- P i net and Q i net will be denoted as P i and Q i , respectively.
- P i net and Q i net will be denoted as P i and Q i , respectively.
- N buses there are a total of 4N variables with 2N constraints given by Eqs. (B.8a)/(B.8b) and Eqs. (B.9a)/(B.9b).
- 2N variables have to be pre-specified. Based on the variables that are pre-specified, buses can be classified into three categories as shown in Table B.1.
- the power flow problem is then to find the unspecified ⁇
- Eqs. (B.8a)/(B.8b) and Eqs. (B.9a)/(B.9b) are nonlinear and no closed-form solution exists
- the Newton Raphson algorithm is used to obtain a solution through iterative linearization.
- the first step is to use initial estimates of ⁇
- a ij,k is the sensitivity coefficients of line i-j with respect to injected power at bus k, p k , and
- Bus 1 is the reference bus and others are load or generator buses. For simplicity but without loss of generality, it is assumed that there is no import or export power. In the absence of thermal limits and transmission losses, generators should be dispatched based on ALSO results, and energy prices at various buses shall be the same (the MCP). However, thermal limits or transmission losses may necessitate the dispatch of a more expensive generator rather than a less expensive alternative.
- LMP locational marginal price
- I the price to serve the next MW of energy at that bus.
- generators are compensated at the bus while load serving entities pay based on the average LMP of a group of buses in an area called a zone.
- the injected power at a bus is the sum of power generated by the generators at that bus, i.e.,
- the objective of the ISO is to minimize the total cost of purchasing power, which is the sum of power purchase costs at different buses, i.e.,
- the Augmented Lagrangian relaxation and surrogate optimization (ALSO) framework may be used.
- the process starts with re-defining offer curves as in Eq. (8):
- the above inequality constraints couple the LMP at a bus with the offers at that bus and will be relaxed by using Lagrange multipliers. Augmented Lagrangian Relaxation
- the subproblem for supply offers related to bus 1 is similarly formed from (B.37) by pulling out all terms related to that offer, i.e.,
- the LMP subproblem for bus n at hour t is formed from Eq. (B.37) by pulling out all terms related to the LMP n (t), i.e.,
- the multipliers will be updated by using either the Multiplier Method or the Subgradient Method, with subgradients in the formulas replaced by surrogate subgradients.
- the DC power flow analysis is to solve the power flow problem with a simplified model given the system topology, transmission parameters, and generation and load distribution.
- the analysis applies the following simplifying approximations to the AC power flow model: Transmission lines have no resistance, the variations of phase angles of bus voltages are small, and the magnitudes of bus voltages are constant. With these assumptions, the reactive power of a transmission line is negligible and the
- active power is a linear function of phase angles of bus voltages:
- P ij ( ⁇ i - ⁇ j ) X ij , for ⁇ ⁇ all ⁇ ⁇ ⁇ i ⁇ ⁇ and ⁇ ⁇ j , ( C ⁇ .1 )
- P ij is the active power from bus i to bus j
- ⁇ i and ⁇ j are the voltage phase angles at bus i and bus j, respectively
- X ij is the reactance of the transmission line between i and j.
- P i net is the net output power
- P i the total generation at bus i
- P i L the load at bus i.
- P TR A ⁇ ⁇ ⁇ , ( C ⁇ .3 )
- P net ⁇ j 1 ⁇ ⁇ ( ⁇ i - ⁇ j )
- X ij B ⁇ ⁇ ⁇ , ( C ⁇ .4 )
- P TR is the N T ⁇ 1 column vector of power flow (N T is the number of transmission lines);
- P net is an (N ⁇ 1) ⁇ 1 column vector of net output power at each bus (N is the number of buses);
- ⁇ is (N ⁇ 1) ⁇ 1 column vector of bus voltage angles with regard to the reference bus;
- Power flow should not exceed their upper limits, i.e., AB ⁇ 1 P net ⁇ P max , (C.6) where P max is a given N T ⁇ 1 column vector of transmission limits.
- a set of linear inequality transmission constraints can therefore be established from Eq. (C.6) by checking the violation or near violation of transmission limits.
- FIG. 5 illustrates a typical data processing system upon which one embodiment of the present invention may be implemented. It will be apparent to those of ordinary skill in the art, however, that other alternative systems of various system architectures may also be used.
- the data processing system illustrated in FIG. 5 includes a bus or other internal communication means 101 for communicating information, and a processor 102 coupled to the bus 101 for processing information.
- the system further comprises a random access memory (RAM) or other volatile storage device 104 (referred to as main memory), coupled to bus 101 for storing information and instructions to be executed by processor 102 .
- Main memory 104 also may be used for storing temporary variables or other intermediate information during execution of instructions by processor 102 .
- the system also comprises a read only memory (ROM) and/or static storage device 106 coupled to bus 101 for storing static information and instructions for processor 102 , and a mass storage device 107 such as a magnetic disk drive or optical disk drive.
- Mass storage device 107 is coupled to bus 101 and is typically used with a computer readable mass storage medium 108 , such as a magnetic or optical disk, for storage of information and program instructions.
- the system may further be coupled to a display device 121 , such as a cathode ray tube (CRT) or a liquid crystal display (LCD) coupled to bus 101 through bus 103 for displaying information to a computer user.
- a display device 121 such as a cathode ray tube (CRT) or a liquid crystal display (LCD) coupled to bus 101 through bus 103 for displaying information to a computer user.
- An alphanumeric input device 122 may also be coupled to bus 101 through bus 103 for communicating information and command selections to processor 102 .
- cursor control 123 such as a mouse, a trackball, stylus, or cursor direction keys coupled to bus 101 through bus 103 for communicating direction information and command selections to processor 102 , and for controlling cursor movement on display device 121 .
- cursor control 123 such as a mouse, a trackball, stylus, or cursor direction keys coupled to bus 101 through bus 103 for communicating direction information and command selections to processor 102 , and for
- a communication device 125 can be coupled to bus 101 through bus 103 for use in accessing other nodes of a network computer system or other computer peripherals.
- This communication device 125 may include any of a number of commercially available networking peripheral devices such as those used for coupling to an Ethernet, token ring, Internet, or wide area network. It may also include any number of commercially available peripheral devices designed to communicate with remote computer peripherals such as scanners, terminals, specialized printers, or audio input/output devices.
- Communication device 125 may also include an RS232 or other conventional serial port, a conventional parallel port, a small computer system interface (SCSI) port or other data communication means.
- SCSI small computer system interface
- Communications device 125 may use a wireless means of data transfer devices such as the infrared IRDA protocol, spread-spectrum, or wireless LAN.
- communication device 125 is used in the preferred embodiment to couple the mobile playback device 212 to the client computer system 214 as described in more detail below.
- the data processing system illustrated in FIG. 5 is an IBM® compatible personal computer (PC), an Apple Macintosh® personal computer, or a SUN® SPARC Workstation.
- Processor may be one of the 80 ⁇ 86 compatible microprocessors such as the 80486 or PENTIUM® brand microprocessors manufactured by INTEL® Corporation of Santa Clara, Calif.
- the software implementing the present invention can be stored in main memory 104 , mass storage device 107 , or other storage medium accessible to processor 102 . It will be apparent to those of ordinary skill in the art that the methods and processes described herein can be implemented as software stored in main memory 104 or read only memory 106 and executed by processor 102 . This software may also be resident on an article of manufacture comprising a computer usable mass storage medium 108 having computer readable program code embodied therein and being readable by the mass storage device 107 and for causing the processor to perform digital information transactions and protocols in accordance with the teachings herein.
- Suitable C++ programming software includes: Borland C++ Builder 6.0 Professional and Microsoft Visual C++6.0 Professional.
Abstract
Description
p i(t)=0, if u i(t)=0, (3)
p imin(t)≦p i(t)≦p imax(t), if u i(t)=1, t=1, 2, . . . , T, (4)
where pimin(t) and pimax(t) are, respectively, the minimum and maximum power levels for supply offer i at hour t.
MCP(t)=max{O i(p i(t),t)), ∀ i such that p i(t)>0}. (5)
In this case, the purchase cost could be significantly higher than the cost obtained from the minimization in Eq. (1), since participants with supply offers lower than the MCP are paid at the MCP.
For notational convenience, a function f(t) can be defined as:
then the new “Pay-at-MCP” objective function may be written as:
In this way, MCP becomes dependent on all the offers as opposed to being dependent on only selected offers, and Eq. (5) can be equivalently written as the following “MCP—Offer constraints” (MCP “minus” Offer) in linear inequality form:
MCP(t)≧O i r(p i(t),t), ∀ i and t, (9a)
or equivalently,
g i(t)≡O i r(p i(t),t)−MCP(t)≦0, ∀ i and t. (9)
The above inequality constraints now couple MCPs with all offers, and may be relaxed by Lagrange multipliers.
Solution of Pay-At-MCP Objective Function
where zi 2(t) is a non-negative slack variable used to convert Eq. (9) into an equality constraint. The above augmented Lagrangian is to be minimized by selecting appropriate {MCP(t)}, {pi(t)}, and {zi 2(t)}. The process is to first select zi 2(t) subject to zi 2(t)≧0, and the resulting augmented Lagrangian can be simplified to:
where {pi(t)} are the decision variables and all other decision variables are taken at their latest available values. This optimization is solved by using backward dynamic programming (BDP), where times (hours) are stages, and the select status (selected or not selected) are states. The startup cost Si(t) is a transition cost which is incurred only when offer i goes from an off state to an on state. All the other costs in Eq. (D.3) are stage-wise costs.
where decision variables other than MCP(t) are taken at their latest available values. We can define:
a i(t)≡ηi(t)+c(O i r(p i(t),t)−MCP(t)). (D.5)
k i(t)≡[ηi(t)/c+O i r(p i(t),t)], (D.6)
the quadratic term in Eq. (D.4) takes zero value. Thus the second term on the right-hand-side of Eq. (D.4) represents many “half quadratics” delineated by {ki(t)} as shown in
S η
λk+1(t)=λk(t)+c k S λ(t), (D.9)
ηi k+1(t)=max(0, ηi k(t)+c k S η
or from the Surrogate Subgradient Method:
λk+1(t)=λk(t)+αk S λ(t), (D.9a)
ηi k+1(t)=max(0, ηi k(t)+αk S η
where αk is an appropriate step size at iteration k.
-
-
Step 10. [Initialize.] Initialize pi(t), MCP(t), and λ(t) by using a priority order loading heuristic. Initialize all other multipliers to zero, and set the penalty parameter ck to an appropriate positive value. -
Step 12. [Solve the MCP sub-problems.] Solve the MCP sub-problems and update the multipliers. -
Step 14. [Solve offer sub-problems.] Solve one or multiple offer sub-problems. -
Step 16. [Update the multipliers.] Update the multipliers and go back toStep 14. After all the offer sub-problems are solved, go toStep 18. -
Step 18. [Check Stopping Criteria.] If stopping criteria have not been satisfied, go toStep 12. Otherwise, go toStep 20. -
Step 20. [Generate feasible solutions.] Use simple heuristics to obtain a feasible solution if the sub-problem solutions obtained are infeasible.
-
TABLE 1 |
Characteristics of Supply Offers for Example 1 |
|
Min | Max | ||||
MW | MW | $/MW | Start Up | ||
Offer |
1 | 5 | 50 | 10 | 0 | |
|
5 | 50 | 20 | 0 | |
min J, with J≡(MCP·p 1 +MCP·p 2). (10)
80=p 1 +p 2. (11)
MCP≧10; (12)
MCP≧20. (13)
where c is a positive penalty coefficient and z1 2 and z2 2 are positive slack variables used to convert the inequality constraints of Eq. (12) and Eq. (13), respectively, into equality constraints.
min L 1, with L 1≡(MCP−λ)p 1+0.5c[p 1 2−2p 1(80−p 2)], (16)
min L 2, with L 2≡(MCP−λ)p 2+0.5c[p 2 2−2p 2(80−p 1)], (17)
S λ=80−p 1 −p 2. (19)
S η1=10−MCP, (20)
S η2=20−MCP, (21)
λk+1=λk +c k S λ. (22)
η1 k+1=max(0, η1 k +c k S η1) (23)
η2 k+1=max(0, η2 k +c k S η2) (24)
TABLE 2 |
Results for Example 1 using the Pay-at- |
Hour |
1, MCP = $20/MW |
MW | $/MW | Start Up | ||
Offer |
1 | 50 | 10 | 0 | |
|
30 | 20 | 0 | |
Total Cost = $1600 | ||||
TABLE 3 |
Supply Offer Parameters for Example 2 |
| Hour | 2, System | |
Demand = 100 MW | Demand = 150 MW |
Min | Max | Start Up | Min | Max | $/ | Start Up | ||
MW | MW | $/MW | Cost | MW | MW | | Cost | |
Offer |
1 | 5 | 50 | 10 | 0 | 5 | 60 | 15 | 0 |
|
5 | 40 | 20 | 0 | 5 | 60 | 20 | 0 |
|
0 | 10 | 65 | 50 | 0 | 30 | 65 | 50 |
|
5 | 60 | 30 | 1800 | 5 | 100 | 30 | 1800 |
TABLE 4 |
Results Using the Pay-As- |
HOUR |
1, MCP = 65$/ | HOUR | 2, MCP = 65$/MW |
Pay-As- | Actual Costs | Pay-As- | Actual Costs | ||||||
MW | $/MW | Offer Costs | Using MCP | MW | $/MW | Offer Costs | Using | ||
Offer |
1 | 50 | 10 | 500 | 3250 | 60 | 15 | 900 | 3900 |
|
40 | 20 | 800 | 2600 | 60 | 20 | 1200 | 3900 |
|
10 | 65 | 700 | 700 | 30 | 65 | 1950 | 1950 |
|
0 | 30 | 0 | 0 | 0 | 30 | 0 | 0 |
Cost | $2,000 | $6,550 | Cost | $4,050 | $9,750 |
Total Pay-As-Offer Costs = $6,050 | |||||
Actual Purchase Costs Using MCPs = $16,300 |
TABLE 5 |
Results Using the Pay-at- |
HOUR |
1, MCP = 30 $/ | HOUR | 2, MCP = 30 $/MW |
Pay-At-MCP | Pay-At-MCP | ||||||
MW | $/MW | Costs | MW | $/MW | Costs | ||
Offer 1 | 50 | 10 | 1500 | 60 | 15 | 1800 |
|
40 | 20 | 1200 | 60 | 20 | 1800 |
|
0 | 65 | 0 | 0 | 65 | 0 |
|
10 | 30 | 2100 | 30 | 30 | 900 |
Cost | $4,800 | Cost | $4,500 |
Total Pay-At-MCP Costs = Actual Purchase Costs = $9,300 |
TABLE 6 |
System Demand Parameters for Example 3 |
| |||
HOUR | DEMAND | ||
1 | 2500 | ||
2 | 2550 | ||
3 | 2570 | ||
4 | 2530 | ||
5 | 2650 | ||
6 | 2700 | ||
7 | 2680 | ||
8 | 2740 | ||
9 | 2850 | ||
10 | 2900 | ||
11 | 3500 | ||
12 | 3700 | ||
13 | 3800 | ||
14 | 3900 | ||
15 | 4200 | ||
16 | 4250 | ||
17 | 4400 | ||
18 | 4500 | ||
19 | 4100 | ||
20 | 3850 | ||
21 | 3400 | ||
22 | 2700 | ||
23 | 2300 | ||
24 | 2350 | ||
TABLE 7 |
Supply Offer Parameters for Example 3 |
|
SUPPLY | MIN | MAX | START | ||
OFFERS | MW | MW | $/ | UP COST | |
1 | 100 | 455 | 30 | 1200 |
2 | 60 | 350 | 34 | 1150 |
3 | 50 | 300 | 35 | 1100 |
4 | 30 | 200 | 37 | 1000 |
5 | 40 | 350 | 40 | 350 |
6 | 40 | 320 | 42 | 350 |
7 | 30 | 240 | 45 | 300 |
8 | 30 | 200 | 47 | 300 |
9 | 20 | 190 | 55 | 180 |
10 | 20 | 180 | 57 | 180 |
11 | 20 | 170 | 58 | 175 |
12 | 20 | 160 | 59 | 160 |
13 | 20 | 150 | 60 | 250 |
14 | 20 | 140 | 62 | 200 |
15 | 20 | 130 | 63 | 150 |
16 | 20 | 125 | 65 | 180 |
17 | 20 | 120 | 66 | 140 |
18 | 20 | 115 | 68 | 140 |
19 | 20 | 110 | 70 | 160 |
20 | 20 | 120 | 75 | 250 |
21 | 20 | 115 | 78 | 250 |
22 | 20 | 110 | 80 | 300 |
23 | 20 | 100 | 90 | 50 |
24 | 15 | 90 | 93 | 50 |
25 | 10 | 80 | 95 | 40 |
TABLE 8 |
Summary of purchase costs for Example 3 |
TOTAL PAY-AS- | TOTAL ACTUAL | |
METHOD | OFFER COST | (MCP) COST |
PAY-AS-OFFER | $3,411,193 | $5,345,541 |
PAY-AT-MCP/ALSO | NOT APPLICABLE | $5,126,769 |
SAVINGS | $218,773 | |
where Pi E(t) is the energy selected for offer i at hour t, and Pm D(t) the energy awarded to bid m at hour t.
{tilde over (S)}i=max{0, G i −M i}, (A.2)
where {tilde over (S)}i is capacity compensation for offer i, and Gi and Mi are, respectively, the requested amount and revenue generated through MCPs. Mathematically,
where Pi E(t), Pi R(t), Pi S(t), and Pi N(t) are, respectively, selected energy, regulation, spinning reserve, and non-spinning reserve prices for offer i at hour t; Ci E(Pi E(t),t), Ci R(Pi R(t),t), Ci S(Pi S(t),t) and Ci N(Pi N(t),t) are the corresponding cost curves; and Si NL(t) and Si(t) are, respectively, no-load and applicable start-up costs of offer i at hour t. In addition,
where MCPE(t), MCPR(t), MCPS(t), and MCPN(t) are, respectively, market clearing prices for energy, regulation, spinning reserve, and non-spinning reserve markets.
where Pi R(t) is the selected regulation for offer i at time t. Similarly, system spinning reserve requirements are:
where PS(t) is system spinning reserve requirement at hour t, and Pi S(t) is the selected spinning reserve for offer i at time t. System non-spinning reserve requirements are:
where PN(t) is system non-spinning reserve requirement at hour t, and Pi N(t) is the selected regulation for offer i at time t.
In the above, Oi E(Pi E(t),t), Oi R(Pi R(t),t), Oi S(Pi S(t),t), Oi N(Pi N(t),t) are, respectively, the offer prices for energy, regulation, spinning reserve, and non-spinning reserve of generation unit i at hour t.
g m D(t)≡MCP E(t−B m r(P m D(T),t)≦0∀i and t, (A.10
Compared to the previous embodiment of the objective function Eq. (6), this formulation has more decision variables, i.e., {MCPE(t)}, {MCPR(t)}, {MCPS(t)}, {MCPN(t)}, {Pi E(t)}, {Pi R(t)}, {Pi S(t)}, {Pi N(t)}. The compensation term {tilde over (S)}i also involves MCPs of energy and ancillary services, and decisions of all selected services of offer i.
Solution Methodology
{tilde over (S)}i≧0, and (A.14)
{tilde over (S)}i≧D i, (A.16)
Also with Transmission Constraints
I=Y·V, (B.1)
where,
S i =V i I i *=P i net +jQ i net. (B.6)
TABLE B.1 |
Bus Classifications |
Pre-specified | Unknown | |||
Bus Classification | Variables | Variables | ||
Slack/Reference/Swing | |V|, δ | P, Q | ||
(usually bus 1) | ||||
Voltage Controlled/PV | |V|, P | δ, Q | ||
(usually generator buses) | ||||
Load/PQ (load buses) | P, Q | |V|, δ | ||
Newton Raphson Method for Power Flow
ΔP(δ,|V|)=[P 2 −P 2(δ,|V|), . . . , P n −P n(δ,|V|)]T, (B.10)
ΔQ(δ,|V|)=[Q m −Q m(δ,|V|), . . . , Q N −Q N(X)]T. (B.11)
Through linearization, the error or correction vector (Δδ, Δ|V|) is obtained by solving the following linear equations:
where J11 is a matrix of ∂P(X)/∂δ, J12 is a matrix of ∂P(X)/∂|V|, J21 is a matrix of ∂Q(X)/∂δ, and J22 is a matrix of ∂Q(X)/∂|V|. The variables δ and |V| are then updated as
δk+1=δk+Δδ, (B.13)
|V| k+1 =|V| k +Δ|V|. (B.14)
The updated values are used in Eqs. (B.8a)/(B.8b) and Eqs. (B.9a)/(B.9b) to compute {PK+1, QK+1}, and the process repeats until the norm of the error vector is less than a specified tolerance. Finally, line flows can be calculated. To compute the line losses, let Pij be the flow in the transmission line from bus i to j and Pji the power flow in the reverse direction. In the absence of losses Pij should be equal to negative Pji and (Pij+Pji) equals zero. The loss in line ij is thus given by the absolute value of (Pij+Pji). The sum of the losses in all the lines gives the total loss in the transmission network.
Branch Flow Inequality Constraints
The above can be expressed in matrix form as follows:
ΔP=[Jpx]Δx, (B.17)
where Jpx is the appropriate Jacobian, and Δx is the vector of variational terms Δ|V| and Δδ. We therefore have:
Δx=[Jpx]−1ΔP. (B.18)
Similarly, the MW flow in a transmission line between bus i and j, Pij, is a function of {|V|, δ}. Let w be the column vector of MW flows on lines whose thermal limits would be violated, i.e.,
w=[MWflowij], (B.19)
then by using Taylor series expansion, the following relationship can be obtained:
Δw=[Jwx]Δx, (B.20)
where Jwx is the appropriate Jacobian. Substituting Eq. (B.18) into Eq. (B.20), we have:
Δw=([J wx ][J px]−1)ΔP. (B.21)
The sensitivity coefficient, Δw/ΔP, is therefore obtained as [Jwx][Jpx]−1.
MWflowij≦MWflowij max, (B.22)
where MWflowij max is the thermal limit for the line between bus i and bus j whose thermal limits would be violated. By using a first order Taylor series expansion around the nominal line flow MWflowij 0 and Eq. (B.21), the following is obtained:
By using ΔP=P−P0, we get:
which can be represented as:
where aij,k is the sensitivity coefficients of line i-j with respect to injected power at bus k, pk, and
Formulation of the Auction Problem with Transmission Constraints
System demand constraints require that the total load requirements plus transmission losses should equal the total power generated, i.e.,
where Pload is the total load/system demand, Plosses the total power loss in the network, and Pn the total generation at bus n. Note that in Eq. (B.28), the injected.power at
h 2(p)=P losses(t)−p 1(t)=0. (B.29)
The branch flow inequality constraints are given by (repeating from (B.25)):
LMP n=(t)=max{O nm(p nm(t),t), ∀ m=1, . . . , M n, such that p nm(t)>0}. (B.31)
Finally, the objective of the ISO is to minimize the total cost of purchasing power, which is the sum of power purchase costs at different buses, i.e.,
Similar to Eq. (B.28), the LMP and injected power at
LMP-Offer Inequality Constraints
LMP-Offer inequality constraints can thus be formed relating the LMP at each bus to the offer curves at that bus, i.e.,
LMP n(t)≧O nm r(p nm(t),t)∀ n, m and t, or equivalently, (B.34)
g nm=(t)≡O nm r(p nm(t),t)−LMP n(t)≦0. (B.35)
The above inequality constraints couple the LMP at a bus with the offers at that bus and will be relaxed by using Lagrange multipliers.
Augmented Lagrangian Relaxation
where L is the number of overloaded lines. By substituting Eq. (B.27) into Eq. (B.36), we obtain:
Offer Subproblems
In Eq. (B.37), {pnm(t)} are the decision variables to be optimized whilst all other variables are taken at their latest available values. The solution process is similar to that of ALSO without transmission constraints.
LMP Subproblems
LMPn is optimized in Eq. (B.40), while all other variables are kept at their latest available values. The solution process is similar to that of ALSO without transmission constraints.
Update of Multipliers
The surrogate subgradient component of Lc with respect to the overloaded line branch flow constraint at time t is given by:
Similarly, the surrogate subgradient component of Lc with respect to the LMP-offer multiplier constraint at time t is given by:
S μ
The multipliers will be updated by using either the Multiplier Method or the Subgradient Method, with subgradients in the formulas replaced by surrogate subgradients. The formulas for the Multiplier Method are:
λk+1(t)=λk(t)+c k S λ(t), (B.42)
ηij k+1(t)=max(0, ηij k(t)+c k S η
μnm k+1(t)=max(0, μnm k(t)+c k S μ
-
- Step 30. Given a set of generators at different buses, use ALSO to determine the generation level for each generator.
-
Step 32. Calculate the power flow based on the current generation level solution using the DCpower flow method 321. -
Step 34. Check for transmission constraint violations.- If branch flow limits have been exceeded go to Step 36, if not, go to
Step 38.
- If branch flow limits have been exceeded go to Step 36, if not, go to
- Step 36. Add the violated constraints as inequality branch flow constraints to ALSO to formulate new problem, and go to
Step 40. -
Step 38. Stop. -
Step 40. Use ALSO to solve the newly formulated problem, and go back to Step 30.
Transmission Constraints Using DC Power Flow Analysis
where Pij is the active power from bus i to bus j; δi and δj are the voltage phase angles at bus i and bus j, respectively; and Xij is the reactance of the transmission line between i and j. The energy balance constraints require:
where Pi net is the net output power, Pi the total generation at bus i, and Pi L the load at bus i.
where PTR is the NT×1 column vector of power flow (NT is the number of transmission lines); Pnet is an (N−1)×1 column vector of net output power at each bus (N is the number of buses); δ is (N−1)×1 column vector of bus voltage angles with regard to the reference bus; and A and B matrices of appropriate dimensions determined by the topology of the network and reactance of transmission lines. With A, B and Pnet given, power flow PTR can be calculated as:
P TR =AB −1 P net. (C.5)
AB−1Pnet≦Pmax, (C.6)
where Pmax is a given NT×1 column vector of transmission limits. A set of linear inequality transmission constraints can therefore be established from Eq. (C.6) by checking the violation or near violation of transmission limits.
-
- Step 30. Given a set of generators at different buses, use ALSO to determine the generation level for each generator.
-
Step 32. Calculate the power flow based on the current generation level solution using the AC power flow method 322. -
Step 34. Check for transmission constraint violations.- If branch flow limits have been exceeded go to Step 36, if not, go to
Step 38.
- If branch flow limits have been exceeded go to Step 36, if not, go to
- Step 36. Add the violated constraints as inequality branch flow constraints to ALSO to formulate new problem, and go to
Step 40. -
Step 38. Stop. -
Step 40. Use ALSO to solve the newly formulated problem, and go back to Step 30.
Standard Processing System Upon which the Present Invention may be Implemented
Claims (12)
λk+1(t)=λk(t)+c k S λ
ηi k+1(t)=max(0,η1 k(t)+c k S η1)
λk+1(t)=λk(t)+αk S λ
ηi k+1(t)=max(0,ηi k(t)+αk S ηi(t))
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